Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells

Richard Gault*, Philip Vance, T. Martin McGinnity, Sonya Coleman, Dermot Kerr

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The retina acts as the primary stage for the encoding of visual stimuli in the central nervous system. It is comprised of numerous functionally distinct cells tuned to particular types of visual stimuli. This work presents an analytical approach to identifying contrast-driven retinal cells. Machine learning approaches as well as traditional regression models are used to represent the input-output behaviour of retinal ganglion cells. The findings of this work demonstrate that it is possible to separate the cells based on how they respond to changes in mean contrast upon presentation of single images. The separation allows us to identify retinal ganglion cells that are likely to have good model performance in a computationally inexpensive way.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Pages635-646
Number of pages12
ISBN (Print)9783030863647
DOIs
Publication statusPublished - 13 Sept 2021
Event30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
Duration: 14 Sept 202117 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12893 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th International Conference on Artificial Neural Networks, ICANN 2021
CityVirtual, Online
Period14/09/202117/09/2021

Bibliographical note

Funding Information:
The authors would like to thank Jian Liu, Tim Gollisch and the “Sensory Processing in the Retina” research group at the Department of Ophthalmology, University of Göttingen who supplied the experimental data as part of the “VISUALISE” project funded under the European Union Seventh Framework Programme (FP7-ICT-2011.9.11); grant number [600954] (“VISUALISE”).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Encoding natural images
  • Identifying cell behaviour
  • Retinal modelling
  • Visual modelling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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